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ORCIDNitesh V. Chawla
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- affiliation: University of Notre Dame, USA
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2020 – today
- 2022
[j81]Jermaine Marshall, Priscilla Jiménez-Pazmino, Ronald A. Metoyer, Nitesh V. Chawla
:
A Survey on Healthy Food Decision Influences Through Technological Innovations. ACM Trans. Comput. Heal. 3(2): 25:1-25:27 (2022)- [j80]Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla:
AttrE2vec: Unsupervised attributed edge representation learning. Inf. Sci. 592: 82-96 (2022) - [j79]Piotr Bielak, Kamil Tagowski, Maciej Falkiewicz, Tomasz Kajdanowicz, Nitesh V. Chawla:
FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings. Knowl. Based Syst. 236: 107453 (2022) - [c187]Kaize Ding, Jundong Li, Nitesh V. Chawla, Huan Liu:
Graph Minimally-supervised Learning. WSDM 2022: 1620-1622 - [i56]Steven J. Krieg, Christian W. Smith, Rusha Chatterjee, Nitesh V. Chawla:
Predicting Terrorist Attacks in the United States using Localized News Data. CoRR abs/2201.04292 (2022) - [i55]Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu:
Few-Shot Learning on Graphs: A Survey. CoRR abs/2203.09308 (2022) - [i54]Zhichun Guo, Jun Tao, Siming Chen, Nitesh V. Chawla, Chaoli Wang:
SD2: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance. CoRR abs/2203.12671 (2022) - [i53]Yijun Tian, Chuxu Zhang, Zhichun Guo, Yihong Ma, Ronald A. Metoyer, Nitesh V. Chawla:
Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks. CoRR abs/2205.12396 (2022) - [i52]Steven J. Krieg, William C. Burgis, Patrick M. Soga, Nitesh V. Chawla:
Deep Ensembles for Graphs with Higher-order Dependencies. CoRR abs/2205.13988 (2022) - [i51]Yijun Tian, Chuxu Zhang, Zhichun Guo, Chao Huang, Ronald A. Metoyer, Nitesh V. Chawla:
RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation. CoRR abs/2205.14005 (2022) - 2021
- [j78]Pablo Robles-Granda, Suwen Lin, Xian Wu, Gonzalo J. Martínez, Stephen M. Mattingly, Edward Moskal, Aaron Striegel, Nitesh V. Chawla, Sidney D'Mello, Julie M. Gregg, Kari Nies, Gloria Mark, Ted Grover, Andrew T. Campbell, Shayan Mirjafari, Koustuv Saha, Munmun De Choudhury, Anind K. Dey:
Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being. IEEE Comput. Intell. Mag. 16(2): 46-61 (2021) - [j77]Steven J. Krieg, Jennifer J. Schnur, Jermaine D. Marshall, Matthew M. Schoenbauer, Nitesh V. Chawla:
Pandemic Pulse: Unraveling and Modeling Social Signals During the COVID-19 Pandemic. Digit. Gov. Res. Pract. 2(2): 19:1-19:9 (2021) - [j76]Munira Syed, Daheng Wang, Meng Jiang, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla:
Unified Representation of Twitter and Online News Using Graph and Entities. Frontiers Big Data 4: 699070 (2021) - [j75]Yijun Tian, Chuxu Zhang, Ronald A. Metoyer, Nitesh V. Chawla:
Recipe Recommendation With Hierarchical Graph Attention Network. Frontiers Big Data 4: 778417 (2021) - [j74]Louis Faust, Keith Feldman, Suwen Lin, Stephen M. Mattingly, Sidney D'Mello, Nitesh V. Chawla:
Examining Response to Negative Life Events Through Fitness Tracker Data. Frontiers Digit. Health 3: 659088 (2021) - [j73]Shayan Mirjafari, Hessam Bagherinezhad, Subigya Nepal, Gonzalo J. Martínez, Koustuv Saha, Mikio Obuchi, Pino G. Audia, Nitesh V. Chawla, Anind K. Dey, Aaron Striegel, Andrew T. Campbell:
Predicting Job Performance Using Mobile Sensing. IEEE Pervasive Comput. 20(4): 43-51 (2021) - [j72]Tianwen Jiang, Qingkai Zeng, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang:
Biomedical Knowledge Graphs Construction From Conditional Statements. IEEE ACM Trans. Comput. Biol. Bioinform. 18(3): 823-835 (2021) - [j71]Daheng Wang, Qingkai Zeng, Nitesh V. Chawla, Meng Jiang:
Modeling Complementarity in Behavior Data with Multi-Type Itemset Embedding. ACM Trans. Intell. Syst. Technol. 12(4): 42:1-42:25 (2021) - [j70]Chuxu Zhang, Huaxiu Yao, Lu Yu, Chao Huang, Dongjin Song, Haifeng Chen, Meng Jiang, Nitesh V. Chawla:
Inductive Contextual Relation Learning for Personalization. ACM Trans. Inf. Syst. 39(3): 35:1-35:22 (2021) - [c186]Yijun Tian, Chuxu Zhang, Ronald A. Metoyer, Nitesh V. Chawla:
Recipe Representation Learning with Networks. CIKM 2021: 1824-1833 - [c185]Suwen Lin, Xian Wu, Nitesh V. Chawla:
motif2vec: Semantic-aware Representation Learning for Wearables' Time Series Data. DSAA 2021: 1-10 - [c184]Beenish M. Chaudhry, Dipanwita Dasgupta, Mona A. Mohamed, Nitesh V. Chawla:
Teaching Tablet Technology to Older Adults. HCI (42) 2021: 168-182 - [c183]Dipanwita Dasgupta, Beenish M. Chaudhry, Nitesh V. Chawla:
A Qualitative Usability Evaluation of Tablets and Accessibility Settings by Older Adults. HCI (42) 2021: 183-204 - [c182]Daheng Wang, Tong Zhao, Nitesh V. Chawla, Meng Jiang:
Dynamic Attributed Graph Prediction with Conditional Normalizing Flows. ICDM 2021: 1385-1390 - [c181]Senthil Kumar, Leman Akoglu, Nitesh V. Chawla, José A. Rodríguez-Serrano, Tanveer A. Faruquie, Saurabh Nagrecha:
Machine Learning in Finance. KDD 2021: 4139-4140 - [c180]Suwen Lin, Louis Faust, Nitesh V. Chawla:
Lan: Learning to Augment Noise Tolerance for Self-report Survey Labels. PerCom 2021: 1-10 - [c179]Kaiwen Dong, Kai Lu, Xin Xia, David A. Cieslak, Nitesh V. Chawla:
An Optimized NL2SQL System for Enterprise Data Mart. ECML/PKDD (5) 2021: 335-350 - [c178]Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, Nitesh V. Chawla:
Few-Shot Graph Learning for Molecular Property Prediction. WWW 2021: 2559-2567 - [i50]Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, Nitesh V. Chawla:
Few-Shot Graph Learning for Molecular Property Prediction. CoRR abs/2102.07916 (2021) - [i49]Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla:
DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data. CoRR abs/2105.02340 (2021) - [i48]Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla:
Graph Barlow Twins: A self-supervised representation learning framework for graphs. CoRR abs/2106.02466 (2021) - 2020
- [j69]Suman Kundu, Tomasz Kajdanowicz, Przemyslaw Kazienko, Nitesh V. Chawla:
Fuzzy Relative Willingness: Modeling Influence of Exogenous Factors in Driving Information Propagation Through a Social Network. IEEE Access 8: 186653-186662 (2020) - [j68]Mandana Saebi, Giovanni Luca Ciampaglia, Lance M. Kaplan, Nitesh V. Chawla:
HONEM: Learning Embedding for Higher Order Networks. Big Data 8(4): 255-269 (2020) - [j67]Mandana Saebi, Jian Xu, Lance M. Kaplan, Bruno Ribeiro, Nitesh V. Chawla:
Efficient modeling of higher-order dependencies in networks: from algorithm to application for anomaly detection. EPJ Data Sci. 9(1): 15 (2020) - [j66]Chao Huang, Dong Wang, Nitesh V. Chawla:
Scalable Uncertainty-Aware Truth Discovery in Big Data Social Sensing Applications for Cyber-Physical Systems. IEEE Trans. Big Data 6(4): 702-713 (2020) - [c177]Chuxu Zhang, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, Nitesh V. Chawla:
Few-Shot Knowledge Graph Completion. AAAI 2020: 3041-3048 - [c176]Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh V. Chawla, Zhenhui Li:
Graph Few-Shot Learning via Knowledge Transfer. AAAI 2020: 6656-6663 - [c175]Pingjie Tang, Meng Jiang, Bryan (Ning) Xia, Jed W. Pitera, Jeffrey Welser, Nitesh V. Chawla:
Multi-Label Patent Categorization with Non-Local Attention-Based Graph Convolutional Network. AAAI 2020: 9024-9031 - [c174]Munira Syed, Daheng Wang, Meng Jiang, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla:
Overcoming Data Sparsity in Predicting User Characteristics from Behavior through Graph Embeddings. ASONAM 2020: 32-36 - [c173]Jennifer J. Schnur, Ryan Karl, Angélica García-Martínez, Meng Jiang, Nitesh V. Chawla:
Imputing Growth Snapshot Similarity in Early Childhood Development: A Tensor Decomposition Approach. BIBM 2020: 729-734 - [c172]Suwen Lin, Louis Faust, Sidney D'Mello, Gonzalo J. Martínez, Nitesh V. Chawla:
MBead: Semi-supervised Multilabel Behaviour Anomaly Detection on Multivariate Temporal Sensory Data. IEEE BigData 2020: 1089-1096 - [c171]Zhichun Guo, Wenhao Yu, Chuxu Zhang, Meng Jiang, Nitesh V. Chawla:
GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction. CIKM 2020: 435-443 - [c170]Xian Wu, Stephen M. Mattingly, Shayan Mirjafari, Chao Huang, Nitesh V. Chawla:
Personalized Imputation on Wearable-Sensory Time Series via Knowledge Transfer. CIKM 2020: 1625-1634 - [c169]Steven J. Krieg, Peter M. Kogge, Nitesh V. Chawla:
GrowHON: A Scalable Algorithm for Growing Higher-order Networks of Sequences. COMPLEX NETWORKS (2) 2020: 485-496 - [c168]Chuxu Zhang, Lu Yu, Mandana Saebi, Meng Jiang, Nitesh V. Chawla:
Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases. EMNLP (Findings) 2020: 580-585 - [c167]Priscilla Jiménez-Pazmino, Trenton Ford, Ronald A. Metoyer, Nitesh V. Chawla:
Identifying Bridge Users: the Knowledge Transfer Agents in Enterprise Collaboration Systems. HICSS 2020: 1-10 - [c166]Steven J. Krieg, Daniel H. Robertson, Meeta P. Pradhan, Nitesh V. Chawla:
Higher-order Networks of Diabetes Comorbidities: Disease Trajectories that Matter. ICHI 2020: 1-11 - [c165]Daheng Wang, Meng Jiang, Munira Syed, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla:
Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors. KDD 2020: 2581-2589 - [c164]Tina Eliassi-Rad, Nitesh V. Chawla, Vittoria Colizza, Lauren Gardner, Marcel Salathé, Samuel V. Scarpino, Joseph T. Wu:
Fighting a Pandemic: Convergence of Expertise, Data Science and Policy. KDD 2020: 3493-3494 - [c163]Chuxu Zhang, Meng Jiang, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla:
Multi-modal Network Representation Learning. KDD 2020: 3557-3558 - [c162]Suwen Lin, Xian Wu, Gonzalo J. Martínez, Nitesh V. Chawla:
Filling Missing Values on Wearable-Sensory Time Series Data. SDM 2020: 46-54 - [c161]Poorna Talkad Sukumar, Gonzalo J. Martínez, Ted Grover, Gloria Mark, Sidney K. D'Mello, Nitesh V. Chawla, Stephen M. Mattingly, Aaron D. Striegel:
Characterizing Exploratory Behaviors on a Personal Visualization Interface Using Interaction Logs. EuroVis (Short Papers) 2020: 79-83 - [c160]Xian Wu, Suleyman Cetintas, Deguang Kong, Miao Lu, Jian Yang, Nitesh V. Chawla:
Learning from Cross-Modal Behavior Dynamics with Graph-Regularized Neural Contextual Bandit. WWW 2020: 995-1005 - [c159]Xian Wu, Chao Huang, Chuxu Zhang, Nitesh V. Chawla:
Hierarchically Structured Transformer Networks for Fine-Grained Spatial Event Forecasting. WWW 2020: 2320-2330 - [e9]Carlotta Demeniconi, Nitesh V. Chawla:
Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020, Cincinnati, Ohio, USA, May 7-9, 2020. SIAM 2020, ISBN 978-1-61197-623-6 [contents]
The conference was canceled because of the coronavirus pandemic, the reviewed papers are published in this volume. - [i47]Xian Wu, Chao Huang, Pablo Robles-Granda, Nitesh V. Chawla:
Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series. CoRR abs/2002.03595 (2020) - [i46]Mandana Saebi, Steven J. Krieg, Chuxu Zhang, Meng Jiang, Nitesh V. Chawla:
Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning. CoRR abs/2003.06050 (2020) - [i45]Steven J. Krieg, Jennifer J. Schnur, Jermaine D. Marshall, Matthew M. Schoenbauer, Nitesh V. Chawla:
Pandemic Pulse: Unraveling and Modeling Social Signals during the COVID-19 Pandemic. CoRR abs/2006.05983 (2020) - [i44]Daheng Wang, Meng Jiang, Munira Syed, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla:
Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors. CoRR abs/2006.06820 (2020) - [i43]Pablo Robles-Granda, Suwen Lin, Xian Wu, Sidney D'Mello, Gonzalo J. Martínez, Koustuv Saha, Kari Nies, Gloria Mark, Andrew T. Campbell, Munmun De Choudhury, Anind D. Dey, Julie M. Gregg, Ted Grover, Stephen M. Mattingly, Shayan Mirjafari, Edward Moskal, Aaron Striegel, Nitesh V. Chawla:
Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being. CoRR abs/2006.08364 (2020) - [i42]Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang:
Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network. CoRR abs/2006.09610 (2020) - [i41]Daheng Wang, Zhihan Zhang, Yihong Ma, Tong Zhao, Tianwen Jiang, Nitesh V. Chawla, Meng Jiang:
Learning Attribute-Structure Co-Evolutions in Dynamic Graphs. CoRR abs/2007.13004 (2020) - [i40]Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla:
AttrE2vec: Unsupervised Attributed Edge Representation Learning. CoRR abs/2012.14727 (2020)
2010 – 2019
- 2019
- [j65]Shuo Wang, Leandro L. Minku, Nitesh V. Chawla, Xin Yao:
Learning from data streams and class imbalance. Connect. Sci. 31(2): 103-104 (2019) - [j64]Shuo Wang, Leandro L. Minku, Nitesh V. Chawla, Xin Yao:
Learning in the presence of class imbalance and concept drift. Neurocomputing 343: 1-2 (2019) - [j63]Shayan Mirjafari, Kizito Masaba, Ted Grover, Weichen Wang, Pino G. Audia, Andrew T. Campbell, Nitesh V. Chawla, Vedant Das Swain, Munmun De Choudhury, Anind K. Dey, Sidney K. D'Mello, Ge Gao, Julie M. Gregg, Krithika Jagannath, Kaifeng Jiang, Suwen Lin, Qiang Liu, Gloria Mark, Gonzalo J. Martínez, Stephen M. Mattingly, Edward Moskal, Raghu Mulukutla, Subigya Nepal, Kari Nies, Manikanta D. Reddy, Pablo Robles-Granda, Koustuv Saha, Anusha Sirigiri, Aaron Striegel:
Differentiating Higher and Lower Job Performers in the Workplace Using Mobile Sensing. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(2): 37:1-37:24 (2019) - [j62]Vedant Das Swain, Koustuv Saha, Hemang Rajvanshy, Anusha Sirigiri, Julie M. Gregg, Suwen Lin, Gonzalo J. Martínez, Stephen M. Mattingly, Shayan Mirjafari, Raghu Mulukutla, Subigya Nepal, Kari Nies, Manikanta D. Reddy, Pablo Robles-Granda, Andrew T. Campbell, Nitesh V. Chawla, Sidney D'Mello, Anind K. Dey, Kaifeng Jiang, Qiang Liu, Gloria Mark, Edward Moskal, Aaron Striegel, Louis Tay, Gregory D. Abowd, Munmun De Choudhury:
A Multisensor Person-Centered Approach to Understand the Role of Daily Activities in Job Performance with Organizational Personas. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(4): 130:1-130:27 (2019) - [j61]Aastha Nigam, Reid A. Johnson, Dong Wang, Nitesh V. Chawla:
Characterizing online health and wellness information consumption: A study. Inf. Fusion 46: 33-43 (2019) - [j60]Shao-Yuan Li, Yuan Jiang, Nitesh V. Chawla, Zhi-Hua Zhou:
Multi-Label Learning from Crowds. IEEE Trans. Knowl. Data Eng. 31(7): 1369-1382 (2019) - [j59]Jun Tao, Martin Imre, Chaoli Wang, Nitesh V. Chawla, Hanqi Guo, Gokhan Sever, Seung Hyun Kim:
Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps. IEEE Trans. Vis. Comput. Graph. 25(1): 1236-1245 (2019) - [c158]Frederick Nwanganga, Nitesh V. Chawla:
Using Structural Similarity to Predict Future Workload Behavior in the Cloud. CLOUD 2019: 132-136 - [c157]Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng Chen, Nitesh V. Chawla:
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data. AAAI 2019: 1409-1416 - [c156]Koustuv Saha, Raghu Mulukutla, Kari Nies, Pablo Robles-Granda, Anusha Sirigiri, Dong Whi Yoo, Pino G. Audia, Andrew T. Campbell, Nitesh V. Chawla, Sidney K. D'Mello, Anind K. Dey, Manikanta D. Reddy, Kaifeng Jiang, Qiang Liu, Gloria Mark, Edward Moskal, Aaron Striegel, Munmun De Choudhury, Vedant Das Swain, Julie M. Gregg, Ted Grover, Suwen Lin, Gonzalo J. Martínez, Stephen M. Mattingly, Shayan Mirjafari:
Imputing Missing Social Media Data Stream in Multisensor Studies of Human Behavior. ACII 2019: 178-184 - [c155]Catherine Markley, Keith Feldman, Nitesh V. Chawla:
Outside the Hospital Walls: Associations of Value Based Care Metrics and Community Health Factors. BHI 2019: 1-4 - [c154]Tianwen Jiang, Zhihan Zhang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang:
CTGA: Graph-based Biomedical Literature Search. BIBM 2019: 395-400 - [c153]Stephen M. Mattingly, Julie M. Gregg, Pino G. Audia, Ayse Elvan Bayraktaroglu, Andrew T. Campbell, Nitesh V. Chawla, Vedant Das Swain, Munmun De Choudhury, Sidney K. D'Mello, Anind K. Dey, Ge Gao, Krithika Jagannath, Kaifeng Jiang, Suwen Lin, Qiang Liu, Gloria Mark, Gonzalo J. Martínez, Kizito Masaba, Shayan Mirjafari, Edward Moskal, Raghu Mulukutla, Kari Nies, Manikanta D. Reddy, Pablo Robles-Granda, Koustuv Saha, Anusha Sirigiri, Aaron Striegel:
The Tesserae Project: Large-Scale, Longitudinal, In Situ, Multimodal Sensing of Information Workers. CHI Extended Abstracts 2019 - [c152]Koustuv Saha, Ayse Elvan Bayraktaroglu, Andrew T. Campbell, Nitesh V. Chawla, Munmun De Choudhury, Sidney K. D'Mello, Anind K. Dey, Ge Gao, Julie M. Gregg, Krithika Jagannath, Gloria Mark, Gonzalo J. Martínez, Stephen M. Mattingly, Edward Moskal, Anusha Sirigiri, Aaron Striegel, Dong Whi Yoo:
Social Media as a Passive Sensor in Longitudinal Studies of Human Behavior and Wellbeing. CHI Extended Abstracts 2019 - [c151]Chao Huang, Baoxu Shi, Xuchao Zhang, Xian Wu, Nitesh V. Chawla:
Similarity-Aware Network Embedding with Self-Paced Learning. CIKM 2019: 2113-2116 - [c150]Chao Huang, Xian Wu, Xuchao Zhang, Suwen Lin, Nitesh V. Chawla:
Deep Prototypical Networks for Imbalanced Time Series Classification under Data Scarcity. CIKM 2019: 2141-2144 - [c149]Munira Syed, Jermaine Marshall, Aastha Nigam, Nitesh V. Chawla:
Gender Prediction Through Synthetic Resampling of User Profiles Using SeqGANs. CSoNet 2019: 363-370 - [c148]Beenish Moalla Chaudhry, Louis Faust, Nitesh V. Chawla:
Development and Evaluation of a Web Application for Prenatal Care Coordinators in the United States. DESRIST 2019: 140-156 - [c147]Munira Syed, Malolan Chetlur, Shazia Afzal, G. Alex Ambrose, Nitesh V. Chawla:
Implicit and Explicit Emotions in MOOCs. EDM 2019 - [c146]Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang:
Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text. EMNLP/IJCNLP (1) 2019: 302-312 - [c145]Frederick Nwanganga, Nitesh V. Chawla, Gregory R. Madey:
Statistical Analysis and Modeling of Heterogeneous Workloads on Amazon's Public Cloud Infrastructure. HICSS 2019: 1-10 - [c144]Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, Nitesh V. Chawla:
Heterogeneous Graph Neural Network. KDD 2019: 793-803 - [c143]Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang:
The Role of: A Novel Scientific Knowledge Graph Representation and Construction Model. KDD 2019: 1634-1642 - [c142]Daheng Wang, Tianwen Jiang, Nitesh V. Chawla, Meng Jiang:
TUBE: Embedding Behavior Outcomes for Predicting Success. KDD 2019: 1682-1690 - [c141]Chao Huang, Xian Wu, Xuchao Zhang, Chuxu Zhang, Jiashu Zhao, Dawei Yin, Nitesh V. Chawla:
Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics. KDD 2019: 2613-2622 - [c140]Munira Syed, Trunojoyo Anggara, Alison Lanski, Xiaojing Duan, G. Alex Ambrose, Nitesh V. Chawla:
Integrated Closed-loop Learning Analytics Scheme in a First Year Experience Course. LAK 2019: 521-530 - [c139]Beenish M. Chaudhry, Louis Faust, Nitesh V. Chawla:
From Design to Development to Evaluation of a Pregnancy App for Low-Income Women in a Community-Based Setting. MobileHCI 2019: 7:1-7:11 - [c138]Louis Faust, Priscilla Jiménez-Pazmino, James K. Holland, Omar Lizardo, David Hachen, Nitesh V. Chawla:
What 30 Days Tells Us About 3 Years: Identifying Early Signs of User Abandonment and Non-Adherence. PervasiveHealth 2019: 216-224 - [c137]Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Nitesh V. Chawla:
Neural Tensor Factorization for Temporal Interaction Learning. WSDM 2019: 537-545 - [c136]Chuxu Zhang, Ananthram Swami, Nitesh V. Chawla:
SHNE: Representation Learning for Semantic-Associated Heterogeneous Networks. WSDM 2019: 690-698 - [c135]Chao Huang, Chuxu Zhang, Jiashu Zhao, Xian Wu, Nitesh V. Chawla, Dawei Yin:
MiST: A Multiview and Multimodal Spatial-Temporal Learning Framework for Citywide Abnormal Event Forecasting. WWW 2019: 717-728 - [e8]Tanya Y. Berger-Wolf, Nitesh V. Chawla:
Proceedings of the 2019 SIAM International Conference on Data Mining, SDM 2019, Calgary, Alberta, Canada, May 2-4, 2019. SIAM 2019, ISBN 978-1-61197-567-3 [contents] - [i39]Tomasz Kajdanowicz, Kamil Tagowski, Maciej Falkiewicz, Piotr Bielak, Przemyslaw Kazienko, Nitesh V. Chawla:
Incremental embedding for temporal networks. CoRR abs/1904.03423 (2019) - [i38]Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang:
Constructing Information-Lossless Biological Knowledge Graphs from Conditional Statements. CoRR abs/1907.00720 (2019) - [i37]Mandana Saebi, Giovanni Luca Ciampaglia, Lance M. Kaplan, Nitesh V. Chawla:
HONEM: Network Embedding Using Higher-Order Patterns in Sequential Data. CoRR abs/1908.05387 (2019) - [i36]Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh V. Chawla, Zhenhui Li:
Graph Few-shot Learning via Knowledge Transfer. CoRR abs/1910.03053 (2019) - [i35]Chuxu Zhang, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, Nitesh V. Chawla:
Few-Shot Knowledge Graph Completion. CoRR abs/1911.11298 (2019) - 2018
- [j58]Pamela Bilo Thomas, Daniel H. Robertson, Nitesh V. Chawla:
Predicting onset of complications from diabetes: a graph based approach. Appl. Netw. Sci. 3(1): 48:1-48:16 (2018) - [j57]Saurabh Nagrecha, Reid A. Johnson, Nitesh V. Chawla:
FraudBuster: Reducing Fraud in an Auto Insurance Market. Big Data 6(1): 3-12 (2018) - [j56]Zheng Yan, Jun Liu, Laurence T. Yang, Nitesh V. Chawla:
Big data fusion in Internet of Things. Inf. Fusion 40: 32-33 (2018) - [j55]Alberto Fernández, Salvador García, Francisco Herrera, Nitesh V. Chawla:
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary. J. Artif. Intell. Res. 61: 863-905 (2018) - [j54]Keith Feldman, Spyros Kotoulas, Nitesh V. Chawla:
TIQS: Targeted Iterative Question Selection for Health Interventions. J. Heal. Informatics Res. 2(3): 205-227 (2018) - [j53]Keith Feldman, Reid A. Johnson, Nitesh V. Chawla:
The State of Data in Healthcare: Path Towards Standardization. J. Heal. Informatics Res. 2(3): 248-271 (2018) - [j52]Hong Huang, Yuxiao Dong, Jie Tang, Hongxia Yang, Nitesh V. Chawla, Xiaoming Fu:
Will Triadic Closure Strengthen Ties in Social Networks? ACM Trans. Knowl. Discov. Data 12(3): 30:1-30:25 (2018) - [j51]Jun Tao, Chaoli Wang, Nitesh V. Chawla, Lei Shi, Seung Hyun Kim:
Semantic Flow Graph: A Framework for Discovering Object Relationships in Flow Fields. IEEE Trans. Vis. Comput. Graph. 24(12): 3200-3213 (2018) - [c134]Keith Feldman, Mayra Duarte, Waldo Mikels-Carrasco, Nitesh V. Chawla:
Leveraging health and wellness platforms to understand childhood obesity: A usability pilot of FitSpace. BHI 2018: 418-421 - [c133]Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Louis Faust, Nitesh V. Chawla:
RESTFul: Resolution-Aware Forecasting of Behavioral Time Series Data. CIKM 2018: 1073-1082 - [c132]Chao Huang, Junbo Zhang, Yu Zheng, Nitesh V. Chawla:
DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction. CIKM 2018: 1423-1432 - [c131]Nuno Moniz, Rita P. Ribeiro, Vítor Cerqueira, Nitesh V. Chawla:
SMOTEBoost for Regression: Improving the Prediction of Extreme Values. DSAA 2018: 150-159 - [c130]Louis Faust, David Hachen, Omar Lizardo, Nitesh V. Chawla:
Quantifying Subjective Well-Being Using Trends in Weekend Activity. ICHI 2018: 123-129 - [c129]Chuxu Zhang, Lu Yu, Xiangliang Zhang, Nitesh V. Chawla:
Task-Guided and Semantic-Aware Ranking for Academic Author-Paper Correlation Inference. IJCAI 2018: 3641-3647 - [c128]Qiyu Zhi, Suwen Lin, Shuai He, Ronald A. Metoyer, Nitesh V. Chawla:
VisPod: Content-Based Audio Visual Navigation. IUI Companion 2018: 10:1-10:2 - [c127]Daheng Wang, Meng Jiang, Qingkai Zeng, Zachary Eberhart, Nitesh V. Chawla:
Multi-Type Itemset Embedding for Learning Behavior Success. KDD 2018: 2397-2406 - [c126]